D21 Longitudinal compensation in the cognitive network in huntington's disease. (13th September 2016) - Gregory, Sarah; Klöppel, Stefan; Long, Jeffrey D; Razi, Adeel; Scheller, Elisa; Minkova, Lora; Dürr, Alexandra; Roos, Raymund AC; Leavitt, Blair R; Mills, James A; Stout, Julie; Scahill, Rachael I; et al.
Abstract : Background: In premanifest Huntington's disease (preHD), disease-related neuronal degeneration is dissociated from the capacity to maintain normal levels of performance during cognitive tasks, which could be explained by compensatory processes. Recently, we devised a novel model of compensation which examined the relationship between disease progression, brain activity and task performance and identified a pattern consistent with asymmetrical compensation in preHD. We now examine the hypotheses that due to changes in neuronal pathology, such compensatory processes will change over time. Aims: To examine possible changes in compensatory processes over time, we extended our cross-sectional model to cover repeated measurements. Methods: Over three annual time points we modelled a) average compensation and b) change in compensation in each case examining the relationships between disease progression using volumetric measures, brain activity using task-based and resting-state fMRI and cognitive task performance. Results: For the average model, we showed weak disease effects centred in the left hemisphere. We also identified a non-significant pattern of asymmetrical compensation consistent with our previous cross-sectional analyses: disease effect in the left hemisphere and compensatory effect in the right. There was, however, very limited evidence of longitudinal change in compensation. Conclusion: While we interpret these findings with considerable caution, they suggest a consistent asymmetrical pattern of compensatory effects. However, the apparent absence of longitudinal change in compensation was more likely due to the fact that interaction models are generally less sensitive to small changes and moreover, the problems that can arise in trying to model compensation per se.